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Remote Sensing And Image Interpretation

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Index-based groundwater vulnerability mapping models using hydrogeological settings: A critical evaluation

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh

Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes.

Land-use/land-cover (LU/LC) change is an important component of global environmental change. The need to understand LU/LC change is essential for regional development and land use management towards sustainable development. To understand LU/LC change, the different LU/LC categories and their spatial as well as temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998–2006), using the analysis of Indian Remote Sensing Satellite (IRS) images. In this, an attempt was made to adopt Markov model for obtaining and understanding LU/LC dynamics. Model performance was evaluated between the empirical LU/LC map extracted from CARTOSAT-1 PAN image and the simulated LU/LC map obtained from the Markov model. The future landscape distribution in the year 2014 and 2022 was derived using a Markov model. This result shows that Markov model and geospatial technology together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization ...

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2013.795502

Land use change modelling using a Markov model and remote sensing

Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The DRASTIC model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. In this study the DRASTIC model is applied for a part of Kancheepuram district, Tamil Nadu, India, to generate a small-scale map of groundwater vulnerability to contamination. The whole area is classified on a scale of very low, low, moderate and high susceptibility to pollution. The model is considered in relation to groundwater quality data and results have shown a strong relationship between DRASTIC specific vulnerability index and nitrate-as-nitrogen concentrations. A groundwater vulnerability map is developed by using the DRASTIC model in a computer based Geographic Information System. The results show that the central part of the study area is classified as a high vulnerable zone and the south and northeastern parts show medium vulnerable zones, and record higher nitrate values.

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model

Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The SINTACS model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. The SINTACS model uses seven environmental parameters (water table depth, effective infiltration, unsaturated conditions, soil media, aquifer hydrogeologic characteristics, hydraulic conductivity and topographic slope) to characterize the hydrogeological setting and evaluate aquifer vulnerability...

Groundwater vulnerability assessment using SINTACS model

The increase in population as well as migration from rural areas has resulted in drastic land use/land cover changes in the city of Tiruchirappalli. Unfortunately, these changes have not been quantified appropriately. This paper gives an overview of the study done to quantify the spread of urban activity from years 1998 to 2009 along the city using remote sensing/GIS technology. The results showed an unruly 41 % increase in urban activity, with a proportionate decrease in agricultural land during the period of study. An attempt was also made to project the built-up land for the next 20 years based on the available trend of urban changes and rate of population.

Analysis of Urban Sprawl Pattern in Tiruchirappalli City Using Applications of Remote Sensing and GIS

Land use/land cover mapping serves as a basic inventory of land resources throughout the world. Detecting and analyzing the quantitative changes along the earth’s surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, and economic and industrial growth. This paper examines the changes in land use/land cover types in Tiruchirappalli city, Tamil Nadu from 1998-2006, using remote sensing technology and Geographic Information Systems (GIS). The spatial and temporal changes of land use/land cover were analyzed through visual interpretation and image classification of IRS remote sensing data collected for years 1998, 2002 and 2006. The results of the study indicated an overall growth in built-up land by 80% over a span of eight years, with an incredible boost (58%) in development from 1998-2002. The area under agricultural wet land was found to have decreased from 3,776 ha to 2,689 ha, with an increase in agricultural dry land from 814 ha to 1,058 ha during this period, suggesting the wastage of land due to excessive use of fertilizers.

Sathees Kumar

Papers

Land use change modelling using a Markov model and remote sensing

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model

Groundwater vulnerability assessment using SINTACS model

Analysis of Urban Sprawl Pattern in Tiruchirappalli City Using Applications of Remote Sensing and GIS

Land Use and Land Cover Assessment along Tiruchirappalli City Using Remote Sensing